Multi-relational multi-view clustering and its applications in cancer subtype identification

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Cancer subtype identification aims to partition the cancer patients into different subgroups with distinct clinical phenotypes, which is important for accurate diagnosis and treatment planning. The recent surge in multi-omics data has spurred research into integrative subtype identification, and multi-view clustering is widely used for identifying the underlying potential subtypes in an unsupervised manner. However, most existing approaches only consider the single-level relations within each view and cannot fully explore the high-order relations across views. In this paper, we propose a new Multi-Relational Multi-View Clustering (MRMVC) method to address these issues, which treats multi-omics data as different views, and thoroughly explores multi-level intra-view and inter-view relations for subtype identification. It fully learns the pairwise similarity between samples based on (1) intra-view global relations that encourage the intra-class cohesion, (2) intra-view local relations that promote the inter-class separability, and (3) inter-view high-order relations that align the multiple graphs, enabling the discovery of intrinsic similarities and enhancing the clustering performance. Experiments on generic datasets and multi-omics cancer datasets illustrate the efficacy and superiority of the proposed method in clustering and identifying more distinct cancer subtypes.